Overview

Dataset statistics

Number of variables9
Number of observations552
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.9 KiB
Average record size in memory72.2 B

Variable types

DateTime1
Numeric8

Dataset

DescriptionReports of cleaned natural gas dataset
URL

Variable descriptions

Natural Gas Price, WellheadPrice from source
Natural Gas Price, CitygatePoint of distribution
Natural Gas Price, Delivered to Consumers, ResidentialAverage price paid for by residential users
Natural Gas Price, Delivered to Consumers, CommercialAverage price paid for by commercial users
Natural Gas Price, Delivered to Consumers, IndustrialAverage price paid for by industrial users

Alerts

Natural Gas Price, Wellhead is highly correlated with Natural Gas Price, Citygate and 4 other fieldsHigh correlation
Natural Gas Price, Citygate is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Residential is highly correlated with Natural Gas Price, Wellhead and 3 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Commercial is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Industrial is highly correlated with Natural Gas Price, Wellhead and 3 other fieldsHigh correlation
Natural Gas Price, Electric Power Sector is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Wellhead is highly correlated with Natural Gas Price, Citygate and 4 other fieldsHigh correlation
Natural Gas Price, Citygate is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Residential is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Commercial is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Industrial is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Electric Power Sector is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Wellhead is highly correlated with Natural Gas Price, Citygate and 4 other fieldsHigh correlation
Natural Gas Price, Citygate is highly correlated with Natural Gas Price, Wellhead and 3 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Residential is highly correlated with Natural Gas Price, Wellhead and 2 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Commercial is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Industrial is highly correlated with Natural Gas Price, Wellhead and 3 other fieldsHigh correlation
Natural Gas Price, Electric Power Sector is highly correlated with Natural Gas Price, Wellhead and 4 other fieldsHigh correlation
Year is highly correlated with Natural Gas Price, Wellhead and 5 other fieldsHigh correlation
Month is highly correlated with Natural Gas Price, Delivered to Consumers, ResidentialHigh correlation
Natural Gas Price, Wellhead is highly correlated with Year and 5 other fieldsHigh correlation
Natural Gas Price, Citygate is highly correlated with Year and 5 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Residential is highly correlated with Year and 6 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Commercial is highly correlated with Year and 5 other fieldsHigh correlation
Natural Gas Price, Delivered to Consumers, Industrial is highly correlated with Year and 5 other fieldsHigh correlation
Natural Gas Price, Electric Power Sector is highly correlated with Year and 5 other fieldsHigh correlation
Date has unique values Unique

Reproduction

Analysis started2022-08-03 17:57:25.515599
Analysis finished2022-08-03 17:57:33.985819
Duration8.47 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct552
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1976-01-01 00:00:00
Maximum2021-12-01 00:00:00
2022-08-03T12:57:34.050161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:34.160752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.5
Minimum1976
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:34.285781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1978
Q11987
median1998.5
Q32010
95-th percentile2019
Maximum2021
Range45
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.2879597
Coefficient of variation (CV)0.006648966574
Kurtosis-1.201137149
Mean1998.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1103172
Variance176.569873
MonotonicityIncreasing
2022-08-03T12:57:34.388145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
197612
 
2.2%
201012
 
2.2%
200112
 
2.2%
200212
 
2.2%
200312
 
2.2%
200412
 
2.2%
200512
 
2.2%
200612
 
2.2%
200712
 
2.2%
200812
 
2.2%
Other values (36)432
78.3%
ValueCountFrequency (%)
197612
2.2%
197712
2.2%
197812
2.2%
197912
2.2%
198012
2.2%
198112
2.2%
198212
2.2%
198312
2.2%
198412
2.2%
198512
2.2%
ValueCountFrequency (%)
202112
2.2%
202012
2.2%
201912
2.2%
201812
2.2%
201712
2.2%
201612
2.2%
201512
2.2%
201412
2.2%
201312
2.2%
201212
2.2%

Month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:34.482146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.455183644
Coefficient of variation (CV)0.5315667144
Kurtosis-1.216928288
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum3588
Variance11.93829401
MonotonicityNot monotonic
2022-08-03T12:57:34.558146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
146
8.3%
246
8.3%
346
8.3%
446
8.3%
546
8.3%
646
8.3%
746
8.3%
846
8.3%
946
8.3%
1046
8.3%
Other values (2)92
16.7%
ValueCountFrequency (%)
146
8.3%
246
8.3%
346
8.3%
446
8.3%
546
8.3%
646
8.3%
746
8.3%
846
8.3%
946
8.3%
1046
8.3%
ValueCountFrequency (%)
1246
8.3%
1146
8.3%
1046
8.3%
946
8.3%
846
8.3%
746
8.3%
646
8.3%
546
8.3%
446
8.3%
346
8.3%

Natural Gas Price, Wellhead
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Price from source

Distinct270
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.846503623
Minimum0.54
Maximum10.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:34.698148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile0.89
Q11.8
median2.46
Q33.35
95-th percentile6.469
Maximum10.79
Range10.25
Interquartile range (IQR)1.55

Descriptive statistics

Standard deviation1.711662119
Coefficient of variation (CV)0.6013208994
Kurtosis3.623537146
Mean2.846503623
Median Absolute Deviation (MAD)0.7
Skewness1.75198663
Sum1571.27
Variance2.929787209
MonotonicityNot monotonic
2022-08-03T12:57:34.822185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3522
 
4.0%
1.8913
 
2.4%
1.9413
 
2.4%
2.7112
 
2.2%
2.8611
 
2.0%
2.2511
 
2.0%
2.5911
 
2.0%
3.0310
 
1.8%
2.5410
 
1.8%
2.4610
 
1.8%
Other values (260)429
77.7%
ValueCountFrequency (%)
0.543
0.5%
0.552
0.4%
0.582
0.4%
0.62
0.4%
0.621
 
0.2%
0.631
 
0.2%
0.641
 
0.2%
0.671
 
0.2%
0.711
 
0.2%
0.751
 
0.2%
ValueCountFrequency (%)
10.791
0.2%
10.361
0.2%
10.331
0.2%
9.961
0.2%
9.891
0.2%
9.081
0.2%
8.951
0.2%
8.871
0.2%
8.631
0.2%
8.211
0.2%

Natural Gas Price, Citygate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Point of distribution

Distinct273
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.495072464
Minimum2.61
Maximum12.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:34.934150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.61
5-th percentile2.83
Q13.2475
median3.97
Q35.2025
95-th percentile8.178
Maximum12.48
Range9.87
Interquartile range (IQR)1.955

Descriptive statistics

Standard deviation1.750402389
Coefficient of variation (CV)0.38940471
Kurtosis3.7004735
Mean4.495072464
Median Absolute Deviation (MAD)0.82
Skewness1.807867032
Sum2481.28
Variance3.063908525
MonotonicityNot monotonic
2022-08-03T12:57:35.035151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0228
 
5.1%
3.9119
 
3.4%
3.6912
 
2.2%
3.9711
 
2.0%
3.9410
 
1.8%
3.9610
 
1.8%
2.929
 
1.6%
4.069
 
1.6%
3.989
 
1.6%
3.888
 
1.4%
Other values (263)427
77.4%
ValueCountFrequency (%)
2.611
 
0.2%
2.671
 
0.2%
2.691
 
0.2%
2.71
 
0.2%
2.711
 
0.2%
2.721
 
0.2%
2.744
0.7%
2.751
 
0.2%
2.762
0.4%
2.771
 
0.2%
ValueCountFrequency (%)
12.481
0.2%
12.451
0.2%
12.161
0.2%
11.851
0.2%
11.571
0.2%
11.051
0.2%
10.81
0.2%
10.771
0.2%
10.261
0.2%
10.21
0.2%

Natural Gas Price, Delivered to Consumers, Residential
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Average price paid for by residential users

Distinct405
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.978331032
Minimum1.98
Maximum20.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:35.147197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.98
5-th percentile4.06
Q15.86
median7.835
Q311.6825
95-th percentile16.688
Maximum20.98
Range19
Interquartile range (IQR)5.8225

Descriptive statistics

Standard deviation4.063063664
Coefficient of variation (CV)0.4525410847
Kurtosis-0.3251565239
Mean8.978331032
Median Absolute Deviation (MAD)2.39
Skewness0.7254482452
Sum4956.038729
Variance16.50848634
MonotonicityNot monotonic
2022-08-03T12:57:35.393352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.37
 
1.3%
3.946
 
1.1%
4.556
 
1.1%
4.536
 
1.1%
3.996
 
1.1%
4.476
 
1.1%
4.56
 
1.1%
4.326
 
1.1%
4.066
 
1.1%
6.845
 
0.9%
Other values (395)492
89.1%
ValueCountFrequency (%)
1.982
 
0.4%
2.353
0.5%
2.4437458931
 
0.2%
2.562
 
0.4%
2.9074917851
 
0.2%
2.982
 
0.4%
3.3712376781
 
0.2%
3.682
 
0.4%
3.8362541071
 
0.2%
3.946
1.1%
ValueCountFrequency (%)
20.981
0.2%
20.771
0.2%
20.251
0.2%
20.171
0.2%
19.941
0.2%
18.911
0.2%
18.561
0.2%
18.411
0.2%
18.371
0.2%
18.271
0.2%

Natural Gas Price, Delivered to Consumers, Commercial
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Average price paid for by commercial users

Distinct333
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.942392867
Minimum1.64
Maximum15.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:35.553355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.64
5-th percentile4.5655
Q15.2975
median5.67
Q38.52
95-th percentile11.2345
Maximum15.64
Range14
Interquartile range (IQR)3.2225

Descriptive statistics

Standard deviation2.350336658
Coefficient of variation (CV)0.3385484952
Kurtosis0.4191607038
Mean6.942392867
Median Absolute Deviation (MAD)1.08
Skewness0.7945952082
Sum3832.200862
Variance5.524082406
MonotonicityNot monotonic
2022-08-03T12:57:35.663894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.6217
 
3.1%
5.612
 
2.2%
5.4910
 
1.8%
5.5710
 
1.8%
5.4710
 
1.8%
5.5310
 
1.8%
5.6710
 
1.8%
5.5410
 
1.8%
4.775
 
0.9%
5.465
 
0.9%
Other values (323)453
82.1%
ValueCountFrequency (%)
1.642
0.4%
2.043
0.5%
2.1434052021
 
0.2%
2.232
0.4%
2.6468104041
 
0.2%
2.732
0.4%
3.1502156061
 
0.2%
3.392
0.4%
3.6551
 
0.2%
42
0.4%
ValueCountFrequency (%)
15.641
0.2%
14.761
0.2%
14.681
0.2%
14.291
0.2%
14.191
0.2%
14.161
0.2%
14.011
0.2%
13.511
0.2%
13.121
0.2%
12.951
0.2%

Natural Gas Price, Delivered to Consumers, Industrial
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Average price paid for by industrial users

Distinct296
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.311452995
Minimum1.24
Maximum13.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:35.773936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.24
5-th percentile2.54
Q13.1075
median4.02
Q34.73
95-th percentile7.9245
Maximum13.06
Range11.82
Interquartile range (IQR)1.6225

Descriptive statistics

Standard deviation1.760560712
Coefficient of variation (CV)0.4083451018
Kurtosis4.120697728
Mean4.311452995
Median Absolute Deviation (MAD)0.88
Skewness1.769622585
Sum2379.922053
Variance3.099574021
MonotonicityNot monotonic
2022-08-03T12:57:35.881014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2918
 
3.3%
4.3111
 
2.0%
4.1510
 
1.8%
4.2410
 
1.8%
4.2110
 
1.8%
4.149
 
1.6%
4.179
 
1.6%
4.259
 
1.6%
35
 
0.9%
3.025
 
0.9%
Other values (286)456
82.6%
ValueCountFrequency (%)
1.242
0.4%
1.53
0.5%
1.5985044491
 
0.2%
1.72
0.4%
1.9570088981
 
0.2%
1.992
0.4%
2.231
 
0.2%
2.261
 
0.2%
2.291
 
0.2%
2.3155133471
 
0.2%
ValueCountFrequency (%)
13.061
0.2%
12.112
0.4%
12.061
0.2%
11.351
0.2%
11.171
0.2%
10.851
0.2%
10.181
0.2%
10.11
0.2%
10.031
0.2%
9.611
0.2%

Natural Gas Price, Electric Power Sector
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct312
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.705434783
Minimum1.06
Maximum16.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-03T12:57:36.000630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.06
5-th percentile1.84
Q12.37
median3.12
Q34.45
95-th percentile7.5645
Maximum16.29
Range15.23
Interquartile range (IQR)2.08

Descriptive statistics

Standard deviation1.962010939
Coefficient of variation (CV)0.529495472
Kurtosis5.240899076
Mean3.705434783
Median Absolute Deviation (MAD)0.865
Skewness1.912437755
Sum2045.4
Variance3.849486925
MonotonicityNot monotonic
2022-08-03T12:57:36.137629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.878
 
1.4%
3.236
 
1.1%
1.846
 
1.1%
3.726
 
1.1%
3.076
 
1.1%
2.225
 
0.9%
2.455
 
0.9%
1.965
 
0.9%
1.95
 
0.9%
2.445
 
0.9%
Other values (302)495
89.7%
ValueCountFrequency (%)
1.062
 
0.4%
1.324
0.7%
1.482
 
0.4%
1.554
0.7%
1.581
 
0.2%
1.654
0.7%
1.694
0.7%
1.71
 
0.2%
1.821
 
0.2%
1.846
1.1%
ValueCountFrequency (%)
16.291
0.2%
12.411
0.2%
11.841
0.2%
11.711
0.2%
11.261
0.2%
10.991
0.2%
10.971
0.2%
10.191
0.2%
9.871
0.2%
9.551
0.2%

Interactions

2022-08-03T12:57:32.675811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:25.755600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.194816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.008347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.959423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.770050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.665921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.658633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:32.890747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:26.022607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.379121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.209102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.146034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.971048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.856931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.878666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:32.988771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:26.189605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.462125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.291541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.232033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.067581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.945902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.965498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:33.084840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:26.348115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.549216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.376060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.311075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.162211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.044343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:32.051611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:33.200997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:26.513490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.638893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.464575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.391076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.256753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.133994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:32.259691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:33.310028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:26.688039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.738345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.685589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.488048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.362752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.289615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:32.362691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:33.420054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:26.861918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.829344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.779421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.590505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.466751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.406617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:32.465832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:33.520692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.027784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:27.916366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:28.867392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:29.678045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:30.562808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:31.528618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T12:57:32.566806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-03T12:57:36.246698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-03T12:57:36.412742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-03T12:57:36.587742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-03T12:57:36.761803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-03T12:57:33.708729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-03T12:57:33.899798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateYearMonthNatural Gas Price, WellheadNatural Gas Price, CitygateNatural Gas Price, Delivered to Consumers, ResidentialNatural Gas Price, Delivered to Consumers, CommercialNatural Gas Price, Delivered to Consumers, IndustrialNatural Gas Price, Electric Power Sector
01976-01-01197610.543.943.945.494.311.55
11976-02-01197620.544.023.995.544.291.65
21976-03-01197630.543.914.065.574.291.69
31976-04-01197640.553.962.352.041.501.32
41976-05-01197650.553.981.981.641.241.06
51976-06-01197660.584.021.981.641.241.06
61976-07-01197670.584.064.325.604.141.84
71976-08-01197680.603.694.305.474.151.87
81976-09-01197690.604.024.475.534.241.89
91976-10-011976100.623.974.505.624.171.96

Last rows

DateYearMonthNatural Gas Price, WellheadNatural Gas Price, CitygateNatural Gas Price, Delivered to Consumers, ResidentialNatural Gas Price, Delivered to Consumers, CommercialNatural Gas Price, Delivered to Consumers, IndustrialNatural Gas Price, Electric Power Sector
5422021-03-01202132.254.0410.517.994.403.40
5432021-04-01202141.893.8412.258.404.003.14
5442021-05-01202151.944.3414.138.964.123.35
5452021-06-01202162.544.8817.739.584.153.57
5462021-07-01202172.595.6019.949.884.734.12
5472021-08-01202182.865.6720.9810.195.014.45
5482021-09-01202192.716.2520.2510.285.575.09
5492021-10-012021103.036.4117.5010.456.845.75
5502021-11-012021113.356.0313.2910.117.035.89
5512021-12-012021123.355.8113.1310.336.745.15